Subdivision interpolating implicit surfaces

Jin, Xiaogang; Sun, Hao; Peng, Qunsheng

doi:10.1016/s0097-8493(03)00149-3

Cited by 20 publications

(6 citation statements)

References 27 publications

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“…Now, building a raw deformable surface mesh based on the acquired discrete points efficiently is considered. For an implicit surface building mission, energy minimization can be solved using RBF (radial basis function), a well-established mathematical tool for solving scattered data interpolation problems [23], which accounts for its popularity in surface recovery applications.…”

Section: Scene Flow-based Environment 3d Reconstructionmentioning

confidence: 99%

Distributed Intelligent Assistance Robotic System with Sensor Networks Based on Robot Technology Middleware

Jia

Dong

et al. 2014

International Journal of Distributed Sensor Networks

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This paper proposes a distributed intelligent assistant robotic system in order to improve the quality of the elderly people's life in the population aging society. The system is composed of embedded distributed sensor networks and multirobot intelligent platform. In the proposed system, we use SP 2 ATM (simultaneous path planning and topological mapping) with RBPF (Rao-Blackwellized particle filter) for path planning and localization of mobile robots. In order to perform service tasks, an accurate and reliable 3D environment map is reconstructed by using an effective 3D reconstruction technique from multiview stereo. Besides, the system provides user human tracking services based on multifeature mean shift under the double-layer locating mechanism. To improve the feasibility and reliability of the system, this proposed system is developed based on distributed control technology RTM (robot technology middleware). This paper presents the architecture of the proposed system and the experimental results verify effectiveness of the approaches.

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Section: Scene Flow-based Environment 3d Reconstructionmentioning

confidence: 99%

Distributed Intelligent Assistance Robotic System with Sensor Networks Based on Robot Technology Middleware

Jia

Dong

et al. 2014

International Journal of Distributed Sensor Networks

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“…Then generate all the necessary points for covering the hole area in its entirety. To guarantee the connectivity of the source skull mesh and the insertion points, we then build an implicit surface to represent the hole using a Radial Basis Function (RBF) [6] [7] and the position of insertion points is adjusted by using the Newton Iteration Method [8] .Finally a Laplacian operator is applied to smooth the triangular mesh surface [9] .…”

Section: System Overviewmentioning

confidence: 99%

“…[8] to get the texture coordinate of non-constrained points according to the constrained points. See the final resultFigure 3(c) and (d).…”

mentioning

confidence: 99%

Craniofacial model reconstruction from skull data based on feature points

Liang

Lin

Jiang

et al. 2009

2009 11th IEEE International Conference on Computer-Aided Design and Computer Graphics

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3D craniofacial model reconstruction from skull data has been a challenging research topic for many years. In this paper we develop a craniofacial reconstruction system based on feature points, and our approach only requires the skull model from a standard 3D scanner and feature points superimposed on the skull data. Our method consists of three stages: hole repairing for the 3D skull model, facial reconstruction with Radial Basis Function(RBF) deformation and texture mapping. A new advancing layer-wise solution algorithm and a template matching method are proposed for repairing big and particular holes on the skull model. The reference face model is then transformed to reconstruct the face model base on marked feature points on the skull model , and we improve the RBF deformation.. The experiments demonstrate the high efficiency and visual realism achieved in our approach.

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“…Velho [14] presented a simple and efficient algorithm through a physical process of simulating particle movement. Jin et al [15] combines subdivision and physical simulation process to render the so-called subdivision interpolating implicit surfaces. Based on Velho's and Jin's work, we propose a piecewise rendering method of the global approximation.…”

Section: Global Surface Visualizationmentioning

confidence: 99%

“…In this paper, the vertices of sub-triangles are named subdivision-points and all the sub-triangles form a subdivision net. In [14] and [15], the projection or mapping of the subdivision-points onto the underlying surface were driven by simulating particles' free movement along gradient descending direction of the surface function. In the paper, however, we use the normals attached to each vertex of the input mesh to find the projected points of the subdivision-points onto the global approximation surface.…”

Section: Global Surface Visualizationmentioning

confidence: 99%

Smooth Approximation to Surface Meshes of Arbitrary Topology with Locally Blended Radial Basis Functions

Pang

Pan

et al. 2006

Computer-Aided Design and Applications

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In this paper, we present a new approach for smooth approximation of surface meshes with arbitrary topology and geometry. The approach is based on the well-known radial basis functions (RBFs) for local shape approximation combined with a blending operator and a family of normalized weight functions for global surface construction. Our method first defines a local approximation using locally supported RBF for every vertex of the input mesh. A single global smooth surface is then constructed by blending the local approximations using weight functions associated with mesh vertices. A projection procedure is employed for visualizing the global surface using the local parameterization defined by barycentric coordinates for each facet of the input mesh. The approach provides a robust and efficient solution for smooth surface construction from various 3D mesh models.

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Subdivision interpolating implicit surfaces

Cited by 20 publications

References 27 publications

Distributed Intelligent Assistance Robotic System with Sensor Networks Based on Robot Technology Middleware

Distributed Intelligent Assistance Robotic System with Sensor Networks Based on Robot Technology Middleware

Craniofacial model reconstruction from skull data based on feature points

Smooth Approximation to Surface Meshes of Arbitrary Topology with Locally Blended Radial Basis Functions

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